Linking textual and contextual features for intelligent cyberbullying detection in social media

The rapid and vast adoption of social media and the 21st century technologies is changing the bullying process., i.e., deliberate misuse of power in relationships through social behavior that intends to cause social and psychological harm. In this context, Cyberbullying can be defined as the process of using technology to hurt someone else by sending hurtful messages, pictures, or comments. A successful Cyberbullying detection technique requires an effective combination of big data (generated from social, open, and private data islands), features, and algorithms. While many Cyberbullying detection techniques exist for working with machine learning data and algorithms, support for thinking of new features (to be extracted from images, images' metadata, and textual information generated around images) remains poor. In this paper, we present an intelligent Cyberbullying detection pipeline to: (i) extract features (from an image, image meta-data and textual content generate); (ii) contextualize the extracted features by developing a crowdsourced feedback loop and drawing on human knowledge; and (iii) combining features using a Neural Network to identify and build potentially useful features. We adopt a typical scenario for analyzing social media content generated on Instagram and Twitter to identify Cyberbullying activities. We discuss how the proposed approach significantly improves the quality of extracted knowledge compared to classical natural language analysis techniques.